Large-scale mixed integer optimization approaches for scheduling airline operations under irregularity

Perhaps no single industry has benefited more from advancements in computation, analytics, and optimization than the airline industry. Operations Research (OR) is now ubiquitous in the way airlines develop their schedules, price their itineraries, manage their fleet, route their aircraft, and schedule their crew. These problems, among others, are well-known to industry practitioners and academics alike and arise within the context of the planning environment which takes place well in advance of the date of departure. One salient feature of the planning environment is that decisions are made in a frictionless environment that do not consider perturbations to an existing schedule. Airline operations are rife with disruptions caused by factors such as convective weather, aircraft failure, air traffic control restrictions, network effects, among other irregularities. Substantially less work in the OR community has been examined within the context of the real-time operational environment. While problems in the planning and operational environments are similar from a mathematical perspective, the complexity of the operational environment is exacerbated by two factors. First, decisions need to be made in as close to real-time as possible. Unlike the planning phase, decision-makers do not have hours of time to return a decision. Secondly, there are a host of operational considerations in which complex rules mandated by regulatory agencies like the Federal Administration Association (FAA), airline requirements, or union rules. Such restrictions often make finding even a feasible set of re-scheduling decisions an arduous task, let alone the global optimum. The goals and objectives of this thesis are found in Chapter 1. Chapter 2 provides an overview airline operations and the current practices of disruption management employed at most airlines. Both the causes and the costs associated with irregular operations are surveyed. The role of airline Operations Control Center (OCC) is discussed in which serves as the real-time decision making environment that is important to understand for the body of this work. Chapter 3 introduces an optimization-based approach to solve the Airline Integrated Recovery (AIR) problem that simultaneously solves re-scheduling decisions for the operating schedule, aircraft routings, crew assignments, and passenger itineraries. The methodology is validated by using real-world industrial data from a U.S. hub-and-spoke regional carrier and we show how the incumbent approach can dominate the incumbent sequential approach in way that is amenable to the operational constraints imposed by a decision-making environment. Computational effort is central to the efficacy of any algorithm present in a real-time decision making environment such as an OCC. The latter two chapters illustrate various methods that are shown to expedite more traditional large-scale optimization methods that are applicable a wide family of optimization problems, including the AIR problem. Chapter 4 shows how delayed constraint generation and column generation may be used simultaneously through use of alternate polyhedra that verify whether or not a given cut that has been generated from a subset of variables remains globally valid. While Benders' decomposition is a well-known algorithm to solve problems exhibiting a block structure, one possible drawback is slow convergence. Expediting Benders' decomposition has been explored in the literature through model reformulation, improving bounds, and cut selection strategies, but little has been studied how to strengthen a standard cut. Chapter 5 examines four methods for the convergence may be accelerated through an affine transformation into the interior of the feasible set, generating a split cut induced by a standard Benders' inequality, sequential lifting, and superadditive lifting over a relaxation of a multi-row system. It is shown that the first two methods yield the most promising results within the context of an AIR model.PhDCommittee Co-Chair: Clarke, John-Paul; Committee Co-Chair: Johnson, Ellis; Committee Member: Ahmed, Shabbir; Committee Member: Clarke, Michael; Committee Member: Nemhauser, Georg

Optimising airline maintenance scheduling decisions

Airline maintenance scheduling (AMS) studies how plans or schedules are constructed to ensure that a fleet is efficiently maintained and that airline operational demands are met. Additionally, such schedules must take into consideration the different regulations airlines are subject to, while minimising maintenance costs. In this thesis, we study different formulations, solution methods, and modelling considerations, for the AMS and related problems to propose two main contributions. First, we present a new type of multi-objective mixed integer linear programming formulation which challenges traditional time discretisation. Employing the concept of time intervals, we efficiently model the airline maintenance scheduling problem with tail assignment considerations. With a focus on workshop resource allocation and individual aircraft flight operations, and the use of a custom iterative algorithm, we solve large and long-term real-world instances (16000 flights, 529 aircraft, 8 maintenance workshops) in reasonable computational time. Moreover, we provide evidence to suggest, that our framework provides near-optimal solutions, and that inter-airline cooperation is beneficial for workshops. Second, we propose a new hybrid solution procedure to solve the aircraft recovery problem. Here, we study how to re-schedule flights and re-assign aircraft to these, to resume airline operations after an unforeseen disruption. We do so while taking operational restrictions into account. Specifically, restrictions on aircraft, maintenance, crew duty, and passenger delay are accounted for. The flexibility of the approach allows for further operational restrictions to be easily introduced. The hybrid solution procedure involves the combination of column generation with learning-based hyperheuristics. The latter, adaptively selects exact or metaheuristic algorithms to generate columns. The five different algorithms implemented, two of which we developed, were collected and released as a Python package (Torres Sanchez, 2020). Findings suggest that the framework produces fast and insightful recovery solutions

Optimisation intégrée des rotations et des blocs mensuels personnalisés des équipages en transport aérien

Le problème de la construction des horaires d’équipage pour les compagnies aériennes consiste à assigner un groupe d’équipage à un ensemble planifié de segments de vols. Ce problème doit également respecter des règles de travail définies par la convention collective et les autorités du transport aérien. Le problème de la construction des horaires d’équipage a reçu une attention particulière en recherche opérationnelle car après le carburant, le coût des équipages constitue la plus grande dépense des compagnies aériennes. En raison de la grande taille du problème et de la complexité des règles de travail, ce problème est traditionnellement traité en deux étapes qui sont résolues séquentiellement : la construction de rotations et la construction de blocs mensuels. La première construit un ensemble de rotations réalisables à coût minimum afin que chaque vol prévu puisse être réalisé par un équipage. Les rotations réalisables sont celles juxtaposant des vols conformément aux règles de la convention collective entres les employés et la compagnie aérienne. La deuxième étape construit des blocs mensuels pour les membres d’équipage en combinant les rotations trouvées précédemment avec les repos, et d’autres activités. Chaque bloc mensuel doit satisfaire certaines règles définies par le contrat de travail. Les membres de l’équipage sont divisés en deux groupes selon leurs rôles et leurs responsabilités : les personnels du poste de pilotage et les personnels de la cabine des passagers. Les pilotes, les copilotes et les mécaniciens de bord font partie du personnel du poste de pilotage. Le personnel du poste de pilotage est qualifié pour piloter un avion ou une famille d’avions. Le capitaine de cabine et les agents de bord font partie des membres de la cabine des passagers. Par le passé, les chercheurs se sont concentrés sur la réduction des coûts associés au personnel du poste de pilotage car leurs salaires sont plus élevés que ceux des membres de la cabine des passagers. Dans cette thèse, nous nous concentrons uniquement sur le personnel du poste de pilotage. La construction des blocs mensuels varie pour chaque compagnie aérienne. Toutefois, on peut classer les méthodes en deux catégories : la construction des blocs anonymes (bidline) et la construction des blocs personnalisés. Pour les blocs anonymes, les horaires sont construits de manière à couvrir toutes les rotations sans connaître les préférences des employés. Les blocs sont ensuite présentés aux membres d’équipage qui sélectionnent les blocs qu’ils veulent faire. Contrairement aux blocs anonymes, les blocs personnalisés tiennent compte des préférences des membres de l’équipage. La construction de ces blocs se fait selon deux objectifs : le rostering et les blocs personnalisés avec séniorité (preferrential bidding). Le premier maximise la satisfaction globale des membres d’équipage sans considérer la séniorité. Le second priorise la satisfaction des membres ayant le plus d’ancienneté. D’un point de vue historique, la construction des blocs anonymes a été l’approche la plus utilisée par les compagnies aériennes nord-américaines alors que la construction des blocs personnalisés a été plus fréquente en Europe. Cependant, les blocs personnalisés sont aujourd’hui une approche de planification utilisée par de plus de compagnies aériennes nord-américaines car ils sont plus avantageux à la fois pour les membres de l’équipage et les compagnies aériennes. Par le passé, le problème de construction des rotations et le problème de construction des blocs mensuels ont été modélisés indépendamment. Bien que cette approche réduise la complexité du problème, elle ne considère pas les contraintes de construction de blocs mensuels lors de la construction des rotations. Ce faisant, il n’est pas possible de garantir une solution optimale pour tous les membres de l’équipage. Plus récemment, des chercheurs ont commencé à intégrer ces problèmes. Le problème de construction intégrée de rotations et de blocs mensuels anonymes pour les pilotes a été étudié par Saddoune et al. Cependant, au meilleur de nos connaissances, il n’existe pas de littérature sur le problème d’intégration de construction des rotations et des blocs mensuels personnalisés. Le premier objectif de cette thèse est de présenter une revue de la littérature sur le problème de construction des horaires d’équipage en transport aérien. De plus, nous présentons un modèle mathématique et une approche de résolution pour le problème séquentiel de construction des blocs mensuels personnalisés. Au meilleur de notre connaissance, aucun modèle permettant de prendre en compte les préférences des pilotes n’a été introduit dans la littérature. Nous avons également observé que peu de chercheurs comparent leurs méthodes sur les mêmes données. Nous proposons donc un ensemble d’instances ainsi qu’un générateur de préférences qui est disponible en ligne pour des fins de comparaison. Dans le deuxième objectif de cette thèse, nous considérons le problème intégré de construction des rotations et des blocs mensuels personnalisés. Nous proposons un algorithme heuristique qui construit simultanément des horaires mensuels pour les pilotes et copilotes, tout en respectant les préférences personnelles et les contraintes de sécurité. L’algorithme proposé alterne entre les problèmes de construction des horaires des pilotes et des copilotes afin d’obtenir des rotations similaires, même lorsque les blocs mensuels sont différents. De plus, en raison des perturbations qui arrivent souvent durant l’opération, nous nous sommes intéressés à développer un algorithme permettant d’obtenir une solution robuste ; c’est-à-dire que nous minimisons la propagation de la perturbation d’un premier vol aux autres vols et aux autres membres d’équipage. La troisième contribution de cette thèse vise à satisfaire cet aspect. Pour ce faire, nous résolvons le problème de mise à jour des blocs mensuels simultanément pour les pilotes et les copilotes. Nous visons à maintenir les services de vols et les rotations en commun pour les pilotes et les copilotes dans les solutions de mise à jour. Nous proposons ainsi un algorithme heuristique qui alterne entre le problème de mise à jour des horaires mensuels des pilotes et des copilotes. Pour résumer, cette thèse étudie le problème de construction intégrée des blocs mensuels personnalisés pour les membres de l’équipage. Nous nous concentrons à la fois sur la planification et sur la mise à jour des blocs mensuels.----------ABSTRACT : The airline crew scheduling problem assigns a group of crew members to a set of scheduled flights. This scheduling problem should respect also a set of safety regulations and collective conventions. The airline crew scheduling has received special attention in Operations Research because after fuel, the cost of crew members is the second largest cost for airlines. Due to complexity, traditionally researchers divided this problem into two steps which are solved sequentially: crew pairing and crew assignment. The former constructs a set of minimum cost anonymous feasible pairings for covering the scheduled flights while pairing régulations are taken into account. The latter combines the anonymous pairings with vacations, preassigned activities, and rest periods over a planning horizon (usually a month) to form new schedules for crew members while satisfying safety regulations. Crew members are divided into two groups based on their roles and responsibilities: the cockpit crew members and the cabin crew members. Cockpit crew members are composed of the pilot (captain), copilot (first officer), and flight engineer (for large fleets). The cockpit crew members are qualified to fly one or a family of aircraft types. The cabin crew members are the cabin captain and the flight attendants. Because cockpit crew members are paid substantially higher than cabin crew members, most of the literature has focused on cockpit crew members. In this thesis, we also focus on cockpit crew members composed of pilots and copilots. Despite crew pairings problem which always aims at constructing anonymous pairings, there are two general approaches that airlines consider when solving the crew assignment problem: constructing bidline schedules or personalized schedules. Bidline schedules are anonymous schedules for which the crew preferences and needs are not taken into account. After constructing bidline schedules for crew members, the airlines announce them to the crew members and crew members select the bidlines according to seniority order. In contrast to bidline schedules, personalized schedules consider crew member’s preferences and needs for constructing and allocating the schedules. There are two general ways for constructing personalized schedules: rostering and seniority-based. The former favors providing a maximum global satisfaction for crew members and does not take crew members seniority into account. The latter prioritizes satisfaction of more senior crew members to the junior ones. From a historical point of view, bidline scheduling has been the most common approach at North American airlines whereas personalized scheduling has been more common in Europe. However, personalized schedules are now becoming a common scheduling approach at american airlines by offering advantages for both crew members and airlines. Each of the crew pairing problem and crew assignment problem were modeled independently. This traditional sequential approach reduces the complexity of crew scheduling problem but does not guarantee a global optimum solution for crew members because the constraints of monthly schedules are not taken into account when the pairings are being constructed. More recently, researchers have started to study the integration of the crew pairing and crew assignment problems. The problem of integrated bidline scheduling for pilots has been studied by Saddoune et al. However, integrated personalized crew scheduling for pilots and copilots simultaneously has not been the subject of study so far. The first objective of this thesis is to present an extensive review of literature about airline crew scheduling problem. In addition, in the context of sequential scheduling approach, we present a mathematical model and solution approach for personalized pilot assignment problem. To the best of our knowledge, this personalized assignment model that takes into account the pilots preferences has not yet been introduced in the literature. Furthermore, we observed that researchers frequently do not compare their methods on the same data due to the lack of access to common data sets. Therefore, we made all the data sets and crew preference generators available online which will allow other researchers to do so. As the second objective in this thesis, we consider the integrated personalized crew scheduling problem that simultaneously constructs monthly schedules for pilots and copilots while respecting the personal preferences and safety constraints. In addition, we are interested to maintain the robustness of the crew schedules due to the real-life perturbations that arrive while the planned schedules are being operated. At the operational level, the pilots and copilots must have similar pairings when possible to prevent the propagation of delays throughout the schedules. We present a heuristic algorithm that alternates between the pilot and copilot scheduling problems in order to obtain similar pairings even when the monthly schedules are different. In real life, various disruption sources such as weather conditions may result in delaying or canceling the scheduled flights. These delayed or canceled flights will affect the crew schedules. Due to delay propagation, robust crew recovery problem is very significant. As the third contribution of this thesis, we solve the recovery problem simultaneously for pilots and copilots where the planned schedules are constructed using personalized scheduling approach. We aim at keeping the duties and pairings in common during the recovery solution process. This aim is satisfied by considering heuristic algorithm that alternates between pilots and copilots recovery problems. The re-scheduled flights are considered to be given as an input data.To summarize, this thesis studies integrated personalized crew scheduling problem, in both planning and operational level, which simultaneously constructs/recovers monthly schedules for both pilots and copilots

09261 Abstracts Collection -- Models and Algorithms for Optimization in Logistics

From June 21 to June 26, 2009 the Dagstuhl Seminar Perspectives Workshop 09261 ``Models and Algorithms for Optimization in Logistics \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

FLIGHT RISK MANAGEMENT AND CREW RESERVE OPTIMIZATION

There are two key concerns in the development process of aviation. One is safety, and the other is cost. An airline running with high safety and low cost must be the most competitive one in the market. This work investigates two research efforts respectively relevant to these two concerns. When building support of a real time Flight Risk Assessment and Mitigation System (FRAMS), a sequential multi-stage approach is developed. The whole risk management process is considered in order to improve the safety of each flight by integrating AHP and FTA technique to describe the framework of all levels of risks through risk score. Unlike traditional fault tree analysis, severity level, time level and synergy effect are taken into account when calculating the risk score for each flight. A risk tree is designed for risk data with flat shape structure and a time sensitive optimization model is developed to support decision making of how to mitigate risk with as little cost as possible. A case study is solved in reasonable time to approve that the model is practical for the real time system. On the other hand, an intense competitive environment makes cost controlling more and more important for airlines. An integrated approach is developed for improving the efficiency of reserve crew scheduling which can contribute to decrease cost. Unlike the other technique, this approach integrates the demand forecasting, reserve pattern generation and optimization. A reserve forecasting tool is developed based on a large data base. The expected value of each type of dropped trip is the output of this tool based on the predicted dropping rate and the total scheduled trips. The rounding step in current applied methods is avoided to keep as much information as possible. The forecasting stage is extended to the optimization stage through the input of these expected values. A novel optimization model with column generation algorithm is developed to generate patterns to cover these expected level reserve demands with minimization to the total cost. The many-to-many covering mode makes the model avoid the influence of forecasting errors caused by high uncertainty as much as possible